Satellite image retrieval using low memory locality sensitive hashing in Euclidean space |
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| Authors: | Ruben Buaba Abdollah Homaifar Mohamed Gebril Eric Kihn Mikhail Zhizhin |
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| Institution: | (1) Autonomous Control and Information Technology Center, Department of Electrical and Computer Engineering, Greensboro, NC 27411, USA;(2) NOAA/NGDC, 325 Broadway, Boulder, CO 80305, USA;(3) Russian Academy of Science CGDS, Moscow, Russia |
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| Abstract: | This paper presents the use of the Low Memory Locality Sensitive Hashing (LMLSH) technique operating in Euclidean space to
build a data structure for the Defense Meteorological Satellite Program (DMSP) satellite imagery database. The LMLSH technique
finds satellite image matches in sublinear search time. The texture feature vectors of the images are extracted using pyramid-structured
wavelet transform coupled with Gaussian central moment technique. These feature vectors and families of hash functions, drawn
randomly and independently from a Gaussian distribution, are used to build hash tables. Given a query, the hash tables are
used to pull out the best matches to that query and this is done in a sublinear search time complexity. When tested, our algorithm
has proven to be approximately twenty six times faster than the Linear Search (LS) algorithm. In addition, the LMLSH algorithm searches about two percent of the entire
database randomly to find the possible matches to any given query without loss of accuracy compared to the absolute best matches
returned by its LS counterpart. |
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